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Most existing approaches for determining serious pathological conditions involve analysis of stained images of human tissue. Recently, unstained methods have been used for classification and analysis of cells in human and mammalian tissues. The classifications accuracies have been have been quite poor. We describe a novel application of genetic algorithms for significantly improving the segmentation and classification of cells in unstained Chinese hamster ovarian image samples. The multiagent soft computing model represents a symbiotic relationship between soft computing agents like genetic algorithms, neural networks and water immersion and morphological agents for segmentation and classification of cells in unstained Chinese hamster ovarian image samples.